library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)
coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster
### load all data ---------------------------------
source("_src/global_vars.R")
# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
# seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_highqc.rds"))
myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data_wrapper <- function(cluster_res) {
cluster_res <- enquo(cluster_res)
as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(meta_tbl, by = "sample") %>%
rename(cluster = !!cluster_res) %>%
mutate(cluster = as.character(cluster),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}
plot_data <- plot_data_wrapper(louvain_cluster)
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | cDC1 | cDC2 | Cycling.M | dissociated | M1.CDKN1C | M1.CXCL10 | M1.S100A8 | M1.S100A9 | M2.MARCO | M2.SELENOP | Mast.cell | mDC | Neutrophil | pDC |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | BATF3 | CD1C | CDC20 | BTG1 | CD52 | CCL8 | APOBEC3A | AREG | CCL20 | A2M | CPA3 | BIRC3 | CSF3R | BCL11A |
| 2 | BTLA | CLEC10A | CDK1 | DNAJB1 | CDKN1C | CXCL10 | FCN1 | EREG | CXCL3 | CCL3L1 | CTSG | CCL19 | CXCR2 | CLIC3 |
| 3 | CLEC9A | FCER1A | MKI67 | DUSP1 | LST1 | CXCL11 | S100A8 | S100A8 | FN1 | CCL4L2 | GATA2 | CCR7 | FCGR3B | GZMB |
| 4 | IRF8 | HLA-DQA1 | PTTG1 | EGR1 | SMIM25 | IFIT1 | S100A9 | S100A9 | HAMP | FCGBP | HPGD | IDO1 | IL1R2 | JCHAIN |
| 5 | XCR1 | S100B | TOP2A | FOS | TCF7L2 | TNFSF10 | VCAN | THBS1 | LYVE1 | SELENOP | HPGDS | MARCKSL1 | NAMPT | PTGDS |
| 6 | FOSB | MARCO | KIT | TXNIP | ||||||||||
| 7 | HSPA1A | PLIN2 | MS4A2 | |||||||||||
| 8 | HSPA1B | TGM2 | TPSAB1 | |||||||||||
| 9 | HSPA6 | TPSB2 | ||||||||||||
| 10 | JUN | |||||||||||||
| 11 | JUNB | |||||||||||||
| 12 | KLF2 | |||||||||||||
| 13 | MT1E | |||||||||||||
| 14 | MT1G | |||||||||||||
| 15 | MT1H | |||||||||||||
| 16 | MT1X |
# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>%
# filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
# marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_highqc_markers_02.tsv"))
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
cluster_n_tbl <- seu_obj$cluster_label %>%
table() %>%
enframe("cluster_label", "cluster_n") %>%
mutate(cluster_nrel = cluster_n/sum(cluster_n))
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
ungroup %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
marker_tbl_annotated <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
left_join(cluster_n_tbl, by = "cluster_label") %>%
select(-cluster, -resolution) %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
arrange(cluster_label, -avg_logFC, p_val_adj)
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated.tsv"))
formattable::formattable(marker_sheet)
| rank | M1.S100A8 | M1.CDKN1C | M1.CXCL10 | M2.SELENOP | M2.MARCO | cDC1 | cDC2 | mDC | pDC | Mast.cell | Cycling.M | dissociated | doublet.Fibroblast | doublet.Fibroblast_1 | doublet.Ovarian.cancer.cell | doublet.Plasma.cell | doublet.T.cell |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | S100A9 | CDKN1C | CXCL10 | SELENOP | MARCO | CPVL | FCER1A | BIRC3 | GZMB | TPSAB1 | STMN1 | MT1G | COL1A1 | MMP9 | SLPI | IGKC | IL32 |
| 2 | S100A8 | SMIM25 | CXCL11 | SLC40A1 | FN1 | CLEC9A | S100B | CCL19 | PTGDS | TPSB2 | H2AFZ | MT1H | COL1A2 | CTSK | WFDC2 | MTRNR2L12 | CCL5 |
| 3 | VCAN | LST1 | CCL8 | A2M | HAMP | LGALS2 | CLEC10A | CCR7 | JCHAIN | CPA3 | CENPF | PLIN2 | COL3A1 | ACP5 | KRT19 | FTL | CD2 |
| 4 | FCN1 | CD52 | IFIT2 | DAB2 | LYVE1 | CPNE3 | CD1C | MARCKSL1 | CLIC3 | HPGD | MKI67 | MT1X | CALD1 | SLC9B2 | LCN2 | CXCL8 | GZMA |
| 5 | S100A12 | LILRB2 | TNFSF10 | CCL3 | CXCL3 | IDO1 | PPA1 | TXN | BCL11A | MS4A2 | HIST1H4C | MT2A | SPARC | ANPEP | KRT18 | NUPR1 | CD3D |
| 6 | THBS1 | FCN1 | IFIT1 | CCL4L2 | TIMP1 | CST3 | HLA-DPB1 | IDO1 | ITM2C | CTSG | TOP2A | CXCL8 | DCN | SPARC | CLU | IGLC2 | NKG7 |
| 7 | LYZ | CFP | ISG15 | APOE | CXCL2 | C1orf54 | HLA-DQA1 | LAMP3 | IRF7 | CD69 | TUBB | MT1E | SELENOP | ANKH | KRT7 | APOC1 | TRAC |
| 8 | EREG | IFITM2 | RSAD2 | CCL3L1 | CCL20 | SNX3 | CD1E | IL7R | LILRA4 | CLU | TUBA1B | HSPA1A | TAGLN | LMNA | KRT8 | MTRNR2L8 | GNLY |
| 9 | AC020656.1 | LILRA5 | IFIT3 | FCGBP | CFD | DNASE1L3 | HLA-DQB1 | PPA1 | TCF4 | HPGDS | TYMS | HSPA6 | C1QB | CD109 | CD24 | MARCO | LTB |
| 10 | AREG | RIPOR2 | GBP1 | SLCO2B1 | PLTP | WDFY4 | HLA-DPA1 | CCL22 | PLAC8 | RHEX | HMGN2 | CCL20 | C1QA | SIGLEC15 | S100A1 | CSF3R | TRBC2 |
| 11 | RETN | APOBEC3A | CXCL9 | RNASE1 | CTSL | IRF8 | AREG | CCL17 | TCL1A | KIT | HMGB2 | HK2 | C1QC | TNFRSF11A | S100A13 | APOE | IL7R |
| 12 | CD55 | LYPD2 | APOBEC3A | C1QB | CYP27A1 | S100B | JAML | EBI3 | IRF8 | SLC18A2 | HMGB1 | ADM | NNMT | ANXA2 | IGFBP2 | PLCG2 | CD3G |
| 13 | MCEMP1 | TCF7L2 | MX1 | C1QC | EMP3 | RGCC | PKIB | FSCN1 | CCDC50 | VWA5A | PCLAF | G0S2 | FOLR2 | VIM | C19orf33 | P4HA1 | TRBC1 |
| 14 | CYP1B1 | CD55 | ISG20 | HTRA1 | S100A8 | CLNK | HLA-DRB1 | NUB1 | IL3RA | GATA2 | NUSAP1 | NUPR1 | ACTA2 | ATP6V0D2 | MDK | GPNMB | CD3E |
| 15 | CD300E | SPN | CCL2 | LGMN | VSIG4 | XCR1 | HLA-DRA | CSF2RA | TSPAN13 | AREG | PTTG1 | DNAJB1 | F13A1 | CD99 | EPCAM | SLC25A37 | GZMK |
| 16 | CD36 | COTL1 | GBP4 | F13A1 | CD163 | HLA-DPB1 | HLA-DQA2 | CST7 | UGCG | IL1RL1 | CKS1B | ERO1A | APOE | MATK | CLDN3 | CTSD | SYNE2 |
| 17 | SLC25A37 | LYST | HERC5 | C1QA | GLUL | CCND1 | HLA-DQB2 | DUSP5 | PLD4 | LTC4S | ASPM | NDRG1 | TMEM176B | SUCNR1 | MMP7 | SORL1 | CXCL13 |
| 18 | FPR1 | STXBP2 | GBP5 | GPR34 | S100A9 | RAB7B | LGALS2 | BASP1 | PPP1R14B | HDC | PCNA | SLC2A3 | SLC40A1 | MMP14 | MAL2 | MALAT1 | ETS1 |
| 19 | MNDA | KLF2 | IFIH1 | CCL4 | CTSB | NAAA | HLA-DRB5 | DAPP1 | C12orf75 | LMO4 | TPX2 | CSTB | IGFBP2 | HCFC1R1 | MUC1 | FTH1 | CD7 |
| 20 | SERPINA1 | POU2F2 | NT5C3A | C3 | EREG | HLA-DPA1 | CST3 | CD83 | MZB1 | RGS13 | SMC4 | HSPA1B | COL6A2 | MYO1B | ELF3 | IGHG1 | KLRB1 |
| 21 | NAMPT | CYTIP | IL4I1 | FOLR2 | PDK4 | BATF3 | LTB | GPR157 | TPM2 | AL157895.1 | TK1 | BNIP3 | GPR34 | DPP4 | LAPTM4B | AC020656.1 | SPOCK2 |
| 22 | S100A4 | WARS | IFI6 | STAB1 | CD14 | HLA-DQB1 | PLAC8 | HLA-DQA1 | DERL3 | IER2 | CDK1 | HILPDA | SLCO2B1 | ITGA2 | S100A16 | APLP2 | CLEC2D |
| 23 | RGS2 | SERPINA1 | LY6E | APOC1 | SOD2 | HLA-DQA1 | CSF2RA | PTPN1 | LTB | ADCYAP1 | DUT | SLC2A1 | A2M | COL6A1 | CRABP2 | IGLC3 | CST7 |
| 24 | S100A6 | FGR | MX2 | MRC1 | LTA4H | TXN | PAK1 | DUSP4 | SPIB | SOCS1 | TUBA1C | CXCL3 | LUM | COL6A2 | BCAM | TNFRSF18 | |
| 25 | CFP | PECAM1 | IL1RN | MAF | SLC2A5 | SLAMF7 | CKLF | LGALS2 | CLEC4C | ANXA1 | BIRC5 | GAPDH | TPM2 | NFATC1 | CP | ITM2A | |
| 26 | CD52 | PLAC8 | SAMD9L | MS4A7 | CD55 | PPA1 | CD1A | HLA-DPB1 | SMPD3 | PPP1R15A | NUCKS1 | ANGPTL4 | C3 | FNBP1L | SNCG | IKZF3 | |
| 27 | MXD1 | S100A4 | PARP14 | TREM2 | FBP1 | TACSTD2 | CST7 | RFTN1 | SERPINF1 | BACE2 | KPNA2 | TIMP1 | CTSC | AK5 | S100A14 | CD69 | |
| 28 | APOBEC3A | IFI30 | OAS1 | MSR1 | KLF2 | SHTN1 | NDRG2 | HLA-DRA | MAP1A | LIF | SMC2 | MT1M | RARRES2 | MYO1D | RBP1 | LCK | |
| 29 | CCR2 | HES4 | DDX58 | IFI27 | VMO1 | VMO1 | IFITM2 | SLCO5A1 | IRF4 | GRAP2 | UBE2C | P4HA1 | COL6A1 | PPIC | TM4SF1 | DUSP4 | |
| 30 | IRS2 | CFD | EPSTI1 | PLD3 | C1QB | ASB2 | IGSF6 | POGLUT1 | SCT | RAC2 | RAN | LDHA | DAB2 | EMP1 | S100A2 | TNFRSF4 | |
| 31 | CSF3R | ARL4A | IFITM3 | NRP1 | CXCL8 | KCNK6 | CIITA | LAD1 | CYB561A3 | MAOB | CDKN3 | FAM162A | HLA-DRB1 | COL27A1 | PERP | GZMH | |
| 32 | RIPOR2 | TIMP1 | STAT1 | AKR1B1 | MT2A | RAB32 | GAPT | TRAF1 | SOX4 | CSF1 | RRM2 | C15orf48 | CD74 | CAVIN3 | DSP | ACAP1 | |
| 33 | IRAK3 | LILRA1 | USP18 | PDK4 | C2 | CADM1 | EEF1B2 | AOC1 | SELL | RHOH | PARP1 | EREG | HLA-DRA | RAI14 | MAL | CD27 | |
| 34 | STXBP2 | CD300E | OASL | IGF1 | TIMD4 | CST7 | CD1D | BCL2L14 | APP | KRT1 | DEK | BNIP3L | IGF1 | AC092484.1 | SMIM22 | TIGIT | |
| 35 | SOCS3 | NAAA | WARS | CTSD | SLC39A8 | BASP1 | PLD4 | CLIC2 | SPCS1 | LAPTM4A | RANBP1 | MARCO | MS4A7 | PPFIBP1 | MUC16 | CD8A | |
| 36 | IL1B | TUBA1A | IFI35 | GYPC | C1QA | CD74 | GPAT3 | SPIB | CCDC186 | SLC45A3 | NASP | VCAN | ITM2B | PHETA1 | CLDN4 | CTSW | |
| 37 | CLEC4E | EMP3 | IFI44L | CPM | FABP5 | HLA-DRB1 | TACSTD2 | AL031316.1 | LAMP5 | LAT | CLSPN | SPP1 | MS4A6A | DNM1 | MSLN | KLRD1 | |
| 38 | CSTA | ADGRE5 | RNF213 | HLA-DRB5 | FTH1 | LSP1 | ALDH2 | NET1 | LINC00996 | UBB | HMMR | GPNMB | HLA-DPB1 | DIXDC1 | CDKN2A | CTLA4 | |
| 39 | FGR | CD48 | XAF1 | CD81 | PSAP | GSTP1 | YWHAH | CD274 | SEL1L3 | LAX1 | TUBB4B | MXI1 | HLA-DPA1 | F2R | ASS1 | EVL | |
| 40 | THBD | TNFRSF1B | SAMD9 | SLC1A3 | FTL | PTPRE | MS4A6A | PTGIR | IGKC | STX3 | CCNB1 | CLEC2B | COL6A3 | NRIP3 | FOLR1 | CD247 | |
| 41 | MEGF9 | CX3CR1 | LAP3 | FCGR2A | TFRC | FGL2 | EEF1A1 | MREG | MAPKAPK2 | RAB27B | CEP55 | CXCL2 | CCDC80 | RUFY4 | PDZK1IP1 | CCND2 | |
| 42 | CD93 | LILRB1 | VAMP5 | EGR2 | COLEC12 | FNBP1 | SPINT2 | SLC7A11 | SEC61B | SMYD3 | H2AFV | VEGFA | POSTN | AKAP6 | SCGB2A1 | RORA | |
| 43 | NCF2 | MYO1G | TNFSF13B | ITM2B | CRIP1 | TAP1 | CD74 | TVP23A | CXCR3 | CNRIP1 | SPP1 | RGCC | MYL9 | C1orf21 | KRT17 | CCL4 | |
| 44 | SH3BGRL3 | HCK | OAS3 | CD74 | C5AR1 | HLA-DRA | COTL1 | LIMCH1 | GPR183 | MLPH | ANP32B | FTH1 | CTHRC1 | ZNF462 | CRYAB | IL2RB | |
| 45 | SELL | CTSS | TAP1 | PLTP | TNFRSF1B | ACTG1 | LST1 | TIFAB | OFD1 | ITM2A | HTRA1 | BTG1 | BGN | STEAP1 | NNMT | CD52 | |
| 46 | HBEGF | NAP1L1 | DYNLT1 | EGR1 | TGM2 | EEF1B2 | FILIP1L | IRF4 | IGHM | AL662860.1 | TMPO | HIF1A-AS2 | NBL1 | LAT | MYL9 | GZMM | |
| 47 | LTA4H | CLEC12A | C15orf48 | GPNMB | MT1E | NAP1L1 | HLA-DMA | SLC41A2 | SLC15A4 | CAVIN2 | ATAD2 | MT1F | THY1 | HTRA3 | SLC34A2 | APOBEC3G | |
| 48 | LILRA5 | VSIR | OAS2 | PLAU | NCF1 | DUSP4 | BASP1 | AUTS2 | PLP2 | CALB2 | ARL6IP1 | MIF | MMP11 | F5 | CRIP2 | CD96 | |
| 49 | DUSP1 | FLNA | APOL6 | OLFML3 | VAMP5 | SUB1 | LIMD2 | ANKRD33B | SELENOS | ITM2C | DTYMK | VIM | CXCL12 | CTTN | ARHGAP29 | RASGRP1 | |
| 50 | JAML | LRRC25 | CMPK2 | AP1B1 | APLP2 | ICAM3 | LSP1 | TNFRSF9 | TXN | TIMP3 | LDHB | SLC2A5 | AEBP1 | SLC25A4 | CTTN | STK17A |
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|dissociated")]
my_subtypes <- my_subtypes[my_subtypes %in% unique(seu_obj$cluster_label)]
my_subtypes <- my_subtypes[my_subtypes %in% names(clrs$cluster_label[[coi]])]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))
signature_modules_cd8 <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)} %>%
setNames(paste0(names(.), ".module"))
signature_modules_cin <- yaml::read_yaml("_data/small/signatures/cgas_sting.yaml") %>%
.[lapply(., length) > 1] %>%
setNames(paste0(names(.), ".module"))
# ## compute expression module scores
# for (i in 1:length(signature_modules_cd8)) {
# seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cd8[i], name = names(signature_modules_cd8)[i])
# seu_obj_sub[[names(signature_modules_cd8)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]]
# seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]] <- NULL
# print(paste(names(signature_modules_cd8)[i], "DONE"))
# }
#
# for (i in 1:length(signature_modules_cin)) {
# seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cin[i], name = names(signature_modules_cin)[i])
# seu_obj_sub[[names(signature_modules_cin)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]]
# seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]] <- NULL
# print(paste(names(signature_modules_cin)[i], "DONE"))
# }
#
#
# ## compute progeny scores
# progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
# as.matrix %>%
# progeny %>%
# as.data.frame %>%
# as.list
#
# names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
#
# for (i in 1:length(progeny_list)) {
# seu_obj_sub <- AddMetaData(seu_obj_sub,
# metadata = progeny_list[[i]],
# col.name = names(progeny_list)[i])
# }
#
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))
marker_top_tbl <- marker_sheet[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "Fraction [%]", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "# cells", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
## subset macrophages
# seu_obj_mp <- seu_obj_sub %>%
# subset(subset = cluster_label %in% grep("^M1|^M2", unique(seu_obj_sub$cluster_label), value = T)) %>%
# RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
#
# write_rds(seu_obj_mp, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Macrophages_processed.rds")
## subset DCs
seu_obj_dc <- seu_obj_sub %>%
subset(subset = cluster_label %in% grep("DC|Mast", unique(seu_obj_sub$cluster_label), value = T)) %>%
RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
write_rds(seu_obj_dc, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/DCs_processed.rds")
seu_obj_dc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/DCs_processed.rds")
seu_obj_mp <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Macrophages_processed.rds")
# root_cell <- "SPECTRUM-OV-031_S1_CD45P_LEFT_FALLOPIAN_TUBE_AACAAGATCATTGCTT"
# seu_obj_mp_sub <- subset(seu_obj_mp, cells = c(root_cell, colnames(seu_obj_mp)[colnames(seu_obj_mp)!=root_cell][-1]))
#
# dc_obj <- DiffusionMap(seu_obj_mp_sub@reductions$harmony@cell.embeddings, k = 100)
# dc_mat <- dc_obj@eigenvectors
# colnames(dc_mat) <- paste0("DC_", 1:ncol(dc_mat))
# seu_obj_mp_sub[["DC"]] <- CreateDimReducObject(embeddings = dc_mat, key = "DC_", assay = DefaultAssay(seu_obj_mp_sub))
#
# write_rds(seu_obj_mp_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Macrophages_processed_filtered.rds")
#
# dpt_obj <- DPT(dc_obj)
# write_rds(dpt_obj, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Macrophages_sub_dpt.rds")
# seu_obj_mp_sub$DPT1 <- dpt_obj$DPT1
#
# write_rds(seu_obj_mp_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Macrophages_processed_filtered.rds")
seu_obj_mp_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Macrophages_processed_filtered.rds")
# plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
# left_join(meta_tbl, by = "sample") %>%
# mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
# tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
# mutate(cell_id = colnames(seu_obj_sub),
# cluster_label = ordered(cluster_label, levels = my_subtypes),
# )
#
# if (cell_sort == "CD45+") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
# }
#
# if (cell_sort == "CD45-") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
# }
#
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
# common_layers_disc +
# ggtitle("Sub cluster") +
# #facet_wrap(~cluster_label) +
# scale_color_manual(values = clrs$cluster_label[[coi]])
#
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
# common_layers_disc +
# ggtitle("Patient") +
# # facet_wrap(~therapy) +
# scale_color_manual(values = clrs$patient_id_short)
#
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# # geom_point(aes(umapharmony_1, umapharmony_2),
# # color = "grey90", size = 0.01,
# # data = select(plot_data_sub_sub, -tumor_supersite)) +
# common_layers_disc +
# ggtitle("Site") +
# # facet_wrap(~therapy) +
# scale_color_manual(values = clrs$tumor_supersite)
#
# write_tsv(select(plot_data_sub_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding_sub.tsv"))
plot_data_mp_sub <- as_tibble(FetchData(seu_obj_mp_sub, c(myfeatures, "cluster_label", "DC_1", "DC_2"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_mp_sub),
cluster_label = ordered(cluster_label, levels = names(clrs$cluster_label[[coi]])))
if (cell_sort == "CD45+") {
plot_data_mp_sub <- filter(plot_data_mp_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_mp_sub <- filter(plot_data_mp_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-03-01
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [2]
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## BiocParallel 1.20.1 2019-12-21 [2]
## bitops 1.0-6 2013-08-17 [2]
## boot 1.3-24 2019-12-20 [3]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## class 7.3-15 2019-01-01 [3]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [2]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## DelayedArray 0.12.2 2020-01-06 [2]
## DEoptimR 1.0-8 2016-11-19 [1]
## desc 1.2.0 2018-05-01 [2]
## destiny * 3.0.1 2020-01-16 [1]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## e1071 1.7-3 2019-11-26 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## foreign 0.8-74 2019-12-26 [3]
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